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Criminisi, A, Blake, A, Rother, C, Shotton, J and Torr, P H S (2007). Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. International Journal of Computer Vision. Kluwer Academic Publishers. 71 89–110

Criminisi, A, Shotton, J, Blake, A and Torr, P (2004). Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing. http://www.research.microsoft.comi http://jamie.shotton.org/work/publications/TechRep2003-59.pdf

Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86

Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 472–480

Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943

Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer, Munich, Germany. 2191 269–276

Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision – ECCV 2002). Springer Verlag. 2351 93–108

Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp. Soc., Vancouver, Canada. 237–244

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad. Verlagsges., Berlin, Germany. 9 117–122

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc. Algebraic Frames for the Perception-Action Cycle. Springer, Kiel. 1888 164–174

Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388--400

Technical Report (451.82 KB)

Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929--1943

Technical Report (1.67 MB)

Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276

Technical Report (324.55 KB)

Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108

Technical Report (636.58 KB)

Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388–400

Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision – ECCV 2004. Springer. 3024 74-86

Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. ijcv. 66 67-81

Cremers, D, Tischhäuser, F, Weickert, J and Schnörr, C (2002). Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional. Int. J. Computer Vision. 50 295–313

Chellappa, R and Machinery., Afor Comput (2010). Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010. ACM International Conference Proceeding Series. ACM

Cerrone, L, Zeilmann, A and Hamprecht, F A (2019). End-to-End Learned Random Walker for Seeded Image Segmentation. CVPR. Proceedings. 12559-12568

Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. Heidelberg University

Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060

Censor, Y, Gibali, A, Lenzen, F and Schnörr, C (2016). The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising. J. Comp. Math. 34 608-623

Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498

Cavallo, A (2002). Four dimensional particle tracking in biological dynamic processes. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/2471/

Carstens, H (1998). Ein Skalenraumverfahren Zur Orts/Wellenzahl-Raum-Analyse Winderzeugter Wasserwellen. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Carlsohn, M F, Menze, B H, Kelm, B Michael, Hamprecht, F A, Kercek, A, Leitner, R and Polder, G (2006). Color image processing. CRC Press. 7(17) 393-419

Jähne, B and Jähne, B (1991). Evaluation of a two-scale model using extensive radar backscatter and wave measurements in a large wind-wave flume. Proceedings IGARSS '91. 2 885--888

Cali, C, Baghabra, J, Boges, D J, Holst, G R, Kreshuk, A, Hamprecht, F A, Srinivasan, M, Lehväslaiho, H and Magistretti, P J (2015). Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues. Journal of Comparative Neurology. 524 23-38